{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88a396e8",
   "metadata": {},
   "source": [
    "### Matched Molecular Pairs\n",
    "This notebook provides an example of the use of Matched Molecular Pairs, a technique described in  \n",
    "\n",
    "Hussain, Jameed, and Ceara Rea. \"Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets.\" *Journal of Chemical Information and Modeling*, **50** (2010): 339-348. https://doi.org/10.1021/ci900450m\n",
    "\n",
    "These papers provide good reviews of the method and its applications. \n",
    "\n",
    "Dossetter, Alexander G., Edward J. Griffen, and Andrew G. Leach. \"Matched molecular pair analysis in drug discovery.\" *Drug Discovery Today*, **18** (2013): 724-731. https://doi.org/10.1016/j.drudis.2013.03.003\n",
    "\n",
    "Wassermann, A. M., Dimova, D., Iyer, P., & Bajorath, J., Advances in computational medicinal chemistry: matched molecular pair analysis. *Drug Development Research*, **73** (2012): 518-527. https://doi.org/10.1002/ddr.21045\n",
    "\n",
    "Tyrchan, Christian, and Emma Evertsson. \"Matched molecular pair analysis in short: algorithms, applications and limitations,\" *Computational and Structural Biotechnology Journal* **15** (2017): 86-90. https://doi.org/10.1016/j.csbj.2016.12.003\n",
    "\n",
    "In this notebook we'll look at a hERG inhibition dataset from the ChEMBL database and examine the impact of specific chemical changes on hERG inhibition. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7127463",
   "metadata": {},
   "source": [
    "### Setup\n",
    "Check to see if we're running Google Colab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64054065",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-05T22:15:05.078785Z",
     "start_time": "2025-05-05T22:15:05.076681Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646a293c",
   "metadata": {},
   "source": [
    "Install the necessary Python libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9c9f07ea7604c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "if IN_COLAB:\n",
    "    !pip install pandas requests rdkit useful_rdkit_utils mols2grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e395b69a25fff1fc",
   "metadata": {},
   "source": [
    "Download the scaffold_finder library from GitHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d56078703bc3f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "if IN_COLAB:\n",
    "    import requests\n",
    "    lib_file = requests.get(\"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/main/sar_analysis/scaffold_finder.py\")\n",
    "    ofs = open(\"scaffold_finder.py\",\"w\")\n",
    "    print(lib_file.text,file=ofs)\n",
    "    ofs.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ac5d25",
   "metadata": {},
   "source": [
    "Import the necessary Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0085b183",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from scaffold_finder import FragmentMol\n",
    "from operator import itemgetter\n",
    "from tqdm.auto import tqdm\n",
    "import useful_rdkit_utils as uru\n",
    "from itertools import combinations\n",
    "import mols2grid\n",
    "from rdkit.Chem import AllChem\n",
    "import seaborn as sns\n",
    "\n",
    "from rdkit.Chem.Draw import rdMolDraw2D\n",
    "import io\n",
    "import base64\n",
    "from IPython.display import HTML\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4cbc642",
   "metadata": {},
   "source": [
    "Enable progress bars in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2fef16a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526bedd1",
   "metadata": {},
   "source": [
    "### Read and Process the Input Data\n",
    "Read in hERG data from ChEMBL.  The format is pretty simple, SMILES, ChEMBL ID, hERG pIC50 (negative log of the IC50)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5aae348b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/main/data/hERG.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db182830",
   "metadata": {},
   "source": [
    "Some useful utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f177c5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_map_nums(mol):\n",
    "    \"\"\"\n",
    "    Remove atom map numbers from a molecule\n",
    "    \"\"\"\n",
    "    for atm in mol.GetAtoms():\n",
    "        atm.SetAtomMapNum(0)\n",
    "\n",
    "def sort_fragments(mol):\n",
    "    \"\"\"\n",
    "    Transform a molecule with multiple fragments into a list of molecules that is sorted by number of atoms\n",
    "    from largest to smallest\n",
    "    \"\"\"\n",
    "    frag_list = list(Chem.GetMolFrags(mol, asMols=True))\n",
    "    [remove_map_nums(x) for x in frag_list]\n",
    "    frag_num_atoms_list = [(x.GetNumAtoms(), x) for x in frag_list]\n",
    "    frag_num_atoms_list.sort(key=itemgetter(0), reverse=True)\n",
    "    return [x[1] for x in frag_num_atoms_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf243ee",
   "metadata": {},
   "source": [
    "Add an RDKit molecule column to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6ec589c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mol'] = df.SMILES.apply(Chem.MolFromSmiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb275059",
   "metadata": {},
   "source": [
    "Remove salts, counterions, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1e2e3f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.mol = df.mol.apply(uru.get_largest_fragment)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c107a1ee",
   "metadata": {},
   "source": [
    "### Decompose Molecules to Scaffolds and Sidechains\n",
    "Convert molecules to scaffold/sidechain pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b119bfa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd498b9107be4f67986e05e594e1a80b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4042 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Core</th>\n",
       "      <th>R_group</th>\n",
       "      <th>Name</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>O=C1NCCN1CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc...</td>\n",
       "      <td>*CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc23)CC1</td>\n",
       "      <td>*N1CCNC1=O</td>\n",
       "      <td>CHEMBL12713</td>\n",
       "      <td>8.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>O=C1NCCN1CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc...</td>\n",
       "      <td>*CN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc23)CC1</td>\n",
       "      <td>*CN1CCNC1=O</td>\n",
       "      <td>CHEMBL12713</td>\n",
       "      <td>8.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O=C1NCCN1CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc...</td>\n",
       "      <td>*N1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc23)CC1</td>\n",
       "      <td>*CCN1CCNC1=O</td>\n",
       "      <td>CHEMBL12713</td>\n",
       "      <td>8.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>O=C1NCCN1CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc...</td>\n",
       "      <td>*c1cn(-c2ccc(F)cc2)c2ccc(Cl)cc12</td>\n",
       "      <td>*C1CCN(CCN2CCNC2=O)CC1</td>\n",
       "      <td>CHEMBL12713</td>\n",
       "      <td>8.025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              SMILES  \\\n",
       "0  O=C1NCCN1CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc...   \n",
       "1  O=C1NCCN1CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc...   \n",
       "2  O=C1NCCN1CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc...   \n",
       "3  O=C1NCCN1CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc...   \n",
       "\n",
       "                                           Core                 R_group  \\\n",
       "0  *CCN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc23)CC1              *N1CCNC1=O   \n",
       "1   *CN1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc23)CC1             *CN1CCNC1=O   \n",
       "2    *N1CCC(c2cn(-c3ccc(F)cc3)c3ccc(Cl)cc23)CC1            *CCN1CCNC1=O   \n",
       "3              *c1cn(-c2ccc(F)cc2)c2ccc(Cl)cc12  *C1CCN(CCN2CCNC2=O)CC1   \n",
       "\n",
       "          Name  pIC50  \n",
       "0  CHEMBL12713  8.025  \n",
       "1  CHEMBL12713  8.025  \n",
       "2  CHEMBL12713  8.025  \n",
       "3  CHEMBL12713  8.025  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_list = []\n",
    "for smiles, name, pIC50, mol in tqdm(df.values):\n",
    "    frag_list = FragmentMol(mol,maxCuts=1)\n",
    "    for _,frag_mol in frag_list:\n",
    "        pair_list = sort_fragments(frag_mol)\n",
    "        tmp_list = [smiles]+[Chem.MolToSmiles(x) for x in pair_list]+[name, pIC50]\n",
    "        row_list.append(tmp_list)\n",
    "row_df = pd.DataFrame(row_list,columns=[\"SMILES\",\"Core\",\"R_group\",\"Name\",\"pIC50\"])\n",
    "row_df.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a739ec60",
   "metadata": {},
   "source": [
    "### Collect Pairs With the Same Scaffold\n",
    "Find pairs of molecules sharing same scaffold (Core) and create a table containg pairs and the difference in pIC50 (Delta column). Add a transform column to the table containing the pair as a reaction transform (Transform column). Note that the SMILES are sorted before being put into the transform.  This provides a canonical ordering. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9817da12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4ed14b08d1b409baf10e9c51529b2cf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/23636 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta_list = []\n",
    "for k,v in tqdm(row_df.groupby(\"Core\")):\n",
    "    if len(v) > 2:\n",
    "        for a,b in combinations(range(0,len(v)),2):\n",
    "            reagent_a = v.iloc[a]\n",
    "            reagent_b = v.iloc[b]\n",
    "            if reagent_a.SMILES == reagent_b.SMILES:\n",
    "                continue\n",
    "            reagent_a, reagent_b = sorted([reagent_a, reagent_b], key=lambda x: x.SMILES)\n",
    "            delta=reagent_b.pIC50-reagent_a.pIC50\n",
    "            delta_list.append(list(reagent_a.values)+list(reagent_b.values)\n",
    "                              +[f\"{reagent_a.R_group.replace('*','*-')}>>{reagent_b.R_group.replace('*','*-')}\",delta])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b62ff7e",
   "metadata": {},
   "source": [
    "Put the pairs into a dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ba5db3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMILES_1</th>\n",
       "      <th>Core_1</th>\n",
       "      <th>R_group_1</th>\n",
       "      <th>Name_1</th>\n",
       "      <th>pIC50_1</th>\n",
       "      <th>SMILES_2</th>\n",
       "      <th>Core_2</th>\n",
       "      <th>Rgroup_1</th>\n",
       "      <th>Name_2</th>\n",
       "      <th>pIC50_2</th>\n",
       "      <th>Transform</th>\n",
       "      <th>Delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCCCN(CC)CC#CC#Cc1ccc(Cl)cc1</td>\n",
       "      <td>*C#CCN(CC)CCCC</td>\n",
       "      <td>*C#Cc1ccc(Cl)cc1</td>\n",
       "      <td>CHEMBL3093979</td>\n",
       "      <td>5.665</td>\n",
       "      <td>CCCCN(CC)CC#CCOCc1ccc(Cl)cc1</td>\n",
       "      <td>*C#CCN(CC)CCCC</td>\n",
       "      <td>*COCc1ccc(Cl)cc1</td>\n",
       "      <td>CHEMBL3093951</td>\n",
       "      <td>5.13</td>\n",
       "      <td>*-C#Cc1ccc(Cl)cc1&gt;&gt;*-COCc1ccc(Cl)cc1</td>\n",
       "      <td>-0.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CCCCN(CC)CC#CCOCc1ccc(Cl)cc1</td>\n",
       "      <td>*C#CCN(CC)CCCC</td>\n",
       "      <td>*COCc1ccc(Cl)cc1</td>\n",
       "      <td>CHEMBL3093951</td>\n",
       "      <td>5.130</td>\n",
       "      <td>CCCCN(CC)CC#CCOc1ccc(Cl)cc1</td>\n",
       "      <td>*C#CCN(CC)CCCC</td>\n",
       "      <td>*COc1ccc(Cl)cc1</td>\n",
       "      <td>CHEMBL3093981</td>\n",
       "      <td>4.84</td>\n",
       "      <td>*-COCc1ccc(Cl)cc1&gt;&gt;*-COc1ccc(Cl)cc1</td>\n",
       "      <td>-0.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCCCN(CC)CC#CCCc1ccc(C)cc1</td>\n",
       "      <td>*C#CCN(CC)CCCC</td>\n",
       "      <td>*CCc1ccc(C)cc1</td>\n",
       "      <td>CHEMBL3093977</td>\n",
       "      <td>5.250</td>\n",
       "      <td>CCCCN(CC)CC#CCOCc1ccc(Cl)cc1</td>\n",
       "      <td>*C#CCN(CC)CCCC</td>\n",
       "      <td>*COCc1ccc(Cl)cc1</td>\n",
       "      <td>CHEMBL3093951</td>\n",
       "      <td>5.13</td>\n",
       "      <td>*-CCc1ccc(C)cc1&gt;&gt;*-COCc1ccc(Cl)cc1</td>\n",
       "      <td>-0.120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SMILES_1          Core_1         R_group_1  \\\n",
       "0  CCCCN(CC)CC#CC#Cc1ccc(Cl)cc1  *C#CCN(CC)CCCC  *C#Cc1ccc(Cl)cc1   \n",
       "1  CCCCN(CC)CC#CCOCc1ccc(Cl)cc1  *C#CCN(CC)CCCC  *COCc1ccc(Cl)cc1   \n",
       "2    CCCCN(CC)CC#CCCc1ccc(C)cc1  *C#CCN(CC)CCCC    *CCc1ccc(C)cc1   \n",
       "\n",
       "          Name_1  pIC50_1                      SMILES_2          Core_2  \\\n",
       "0  CHEMBL3093979    5.665  CCCCN(CC)CC#CCOCc1ccc(Cl)cc1  *C#CCN(CC)CCCC   \n",
       "1  CHEMBL3093951    5.130   CCCCN(CC)CC#CCOc1ccc(Cl)cc1  *C#CCN(CC)CCCC   \n",
       "2  CHEMBL3093977    5.250  CCCCN(CC)CC#CCOCc1ccc(Cl)cc1  *C#CCN(CC)CCCC   \n",
       "\n",
       "           Rgroup_1         Name_2  pIC50_2  \\\n",
       "0  *COCc1ccc(Cl)cc1  CHEMBL3093951     5.13   \n",
       "1   *COc1ccc(Cl)cc1  CHEMBL3093981     4.84   \n",
       "2  *COCc1ccc(Cl)cc1  CHEMBL3093951     5.13   \n",
       "\n",
       "                              Transform  Delta  \n",
       "0  *-C#Cc1ccc(Cl)cc1>>*-COCc1ccc(Cl)cc1 -0.535  \n",
       "1   *-COCc1ccc(Cl)cc1>>*-COc1ccc(Cl)cc1 -0.290  \n",
       "2    *-CCc1ccc(C)cc1>>*-COCc1ccc(Cl)cc1 -0.120  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\"SMILES_1\",\"Core_1\",\"R_group_1\",\"Name_1\",\"pIC50_1\",\n",
    "       \"SMILES_2\",\"Core_2\",\"Rgroup_1\",\"Name_2\",\"pIC50_2\",\n",
    "       \"Transform\",\"Delta\"]\n",
    "delta_df = pd.DataFrame(delta_list,columns=cols)\n",
    "delta_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de5bf1a",
   "metadata": {},
   "source": [
    "### Collect Frequently Occuring Pairs\n",
    "Summarize reaction transforms that occur more than **min_transform_occurrence** times. Collect the deltas and calculate and store their mean.  Create a reaction molecule (rxn_mol) for display. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ff35eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_transform_occurrence = 5\n",
    "mmp_list = []\n",
    "for k,v in delta_df.groupby(\"Transform\"):\n",
    "    if len(v) > min_transform_occurrence:\n",
    "        mmp_list.append([k,len(v),v.Delta.values])\n",
    "mmp_df = pd.DataFrame(mmp_list,columns=[\"Transform\",\"Count\",\"Deltas\"]) \n",
    "mmp_df['idx'] = range(0,len(mmp_df))\n",
    "mmp_df['mean_delta'] = [x.mean() for x in mmp_df.Deltas]\n",
    "mmp_df['rxn_mol'] = mmp_df.Transform.apply(AllChem.ReactionFromSmarts,useSmiles=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e37e505",
   "metadata": {},
   "source": [
    "Sort **mmp_df** to find the most interesting pairs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "07752c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transform</th>\n",
       "      <th>Count</th>\n",
       "      <th>Deltas</th>\n",
       "      <th>idx</th>\n",
       "      <th>mean_delta</th>\n",
       "      <th>rxn_mol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>*-Cc1ccc(Cl)cc1&gt;&gt;*-Cc1ccccc1</td>\n",
       "      <td>8</td>\n",
       "      <td>[-0.46999999999999975, -1.7349999999999994, -1...</td>\n",
       "      <td>41</td>\n",
       "      <td>-1.104375</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>*-c1ccc(F)cc1&gt;&gt;*-c1cccnc1</td>\n",
       "      <td>6</td>\n",
       "      <td>[-0.7600000000000007, -1.1599999999999993, -1....</td>\n",
       "      <td>78</td>\n",
       "      <td>-0.913333</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>*-c1ccc(F)cc1&gt;&gt;*-c1ccccn1</td>\n",
       "      <td>8</td>\n",
       "      <td>[0.0, -0.5500000000000007, -1.2199999999999998...</td>\n",
       "      <td>77</td>\n",
       "      <td>-0.722292</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>*-c1ccc(Cl)cc1&gt;&gt;*-c1ccccc1</td>\n",
       "      <td>17</td>\n",
       "      <td>[0.1200000000000001, -0.5599999999999996, -0.4...</td>\n",
       "      <td>72</td>\n",
       "      <td>-0.641863</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>*-c1cccc(Cl)c1&gt;&gt;*-c1ccccc1</td>\n",
       "      <td>7</td>\n",
       "      <td>[0.009999999999999787, -0.26666666666999994, -...</td>\n",
       "      <td>87</td>\n",
       "      <td>-0.615238</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>*-CC&gt;&gt;*-c1ccccc1</td>\n",
       "      <td>8</td>\n",
       "      <td>[0.6099999999999994, 0.39000000000000057, 1.01...</td>\n",
       "      <td>34</td>\n",
       "      <td>0.565000</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>*-OC&gt;&gt;*-C#N</td>\n",
       "      <td>13</td>\n",
       "      <td>[0.0600000000000005, 0.2999999999999998, 0.200...</td>\n",
       "      <td>51</td>\n",
       "      <td>0.591923</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>*-C#N&gt;&gt;*-OC(F)(F)F</td>\n",
       "      <td>6</td>\n",
       "      <td>[0.5999999999999996, 0.6999999999999993, 0.660...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.643333</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>*-OC&gt;&gt;*-OC(F)(F)F</td>\n",
       "      <td>10</td>\n",
       "      <td>[1.1600000000000001, -0.27000000000000046, 0.8...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.653000</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>*-c1ccccc1&gt;&gt;*-c1ccccc1Cl</td>\n",
       "      <td>6</td>\n",
       "      <td>[0.10000000000000053, 0.8600000000000003, 1.10...</td>\n",
       "      <td>92</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>&lt;rdkit.Chem.rdChemReactions.ChemicalReaction o...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Transform  Count  \\\n",
       "41  *-Cc1ccc(Cl)cc1>>*-Cc1ccccc1      8   \n",
       "78     *-c1ccc(F)cc1>>*-c1cccnc1      6   \n",
       "77     *-c1ccc(F)cc1>>*-c1ccccn1      8   \n",
       "72    *-c1ccc(Cl)cc1>>*-c1ccccc1     17   \n",
       "87    *-c1cccc(Cl)c1>>*-c1ccccc1      7   \n",
       "..                           ...    ...   \n",
       "34              *-CC>>*-c1ccccc1      8   \n",
       "51                   *-OC>>*-C#N     13   \n",
       "6             *-C#N>>*-OC(F)(F)F      6   \n",
       "56             *-OC>>*-OC(F)(F)F     10   \n",
       "92      *-c1ccccc1>>*-c1ccccc1Cl      6   \n",
       "\n",
       "                                               Deltas  idx  mean_delta  \\\n",
       "41  [-0.46999999999999975, -1.7349999999999994, -1...   41   -1.104375   \n",
       "78  [-0.7600000000000007, -1.1599999999999993, -1....   78   -0.913333   \n",
       "77  [0.0, -0.5500000000000007, -1.2199999999999998...   77   -0.722292   \n",
       "72  [0.1200000000000001, -0.5599999999999996, -0.4...   72   -0.641863   \n",
       "87  [0.009999999999999787, -0.26666666666999994, -...   87   -0.615238   \n",
       "..                                                ...  ...         ...   \n",
       "34  [0.6099999999999994, 0.39000000000000057, 1.01...   34    0.565000   \n",
       "51  [0.0600000000000005, 0.2999999999999998, 0.200...   51    0.591923   \n",
       "6   [0.5999999999999996, 0.6999999999999993, 0.660...    6    0.643333   \n",
       "56  [1.1600000000000001, -0.27000000000000046, 0.8...   56    0.653000   \n",
       "92  [0.10000000000000053, 0.8600000000000003, 1.10...   92    0.708333   \n",
       "\n",
       "                                              rxn_mol  \n",
       "41  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "78  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "77  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "72  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "87  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "..                                                ...  \n",
       "34  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "51  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "6   <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "56  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "92  <rdkit.Chem.rdChemReactions.ChemicalReaction o...  \n",
       "\n",
       "[103 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmp_df.sort_values(\"mean_delta\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ef9a2b",
   "metadata": {},
   "source": [
    "To make life simpler, create an index that links the two dataframes **mmp_df** and **delta_df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3838a271",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary that links the SMILES for an MMP to its index value\n",
    "transform_dict = dict([(a,b) for a,b in mmp_df[[\"Transform\",\"idx\"]].values])\n",
    "# Add the index column to delta_df\n",
    "delta_df['idx'] = [transform_dict.get(x) for x in delta_df.Transform]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1ffabd",
   "metadata": {},
   "source": [
    "### Visualize the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c95c399d",
   "metadata": {},
   "source": [
    "Visualize one of the transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f139b846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdChemReactions.ChemicalReaction at 0x28e872eb0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_idx = 33\n",
    "mmp_df.rxn_mol.values[current_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185efa3b",
   "metadata": {},
   "source": [
    "Plot the distribution of deltas as a [stripplot](https://seaborn.pydata.org/generated/seaborn.stripplot.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e3d3f76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': (3, 1)})\n",
    "sns.set_style('whitegrid')\n",
    "ax = sns.stripplot(x=mmp_df.Deltas.values[current_idx])\n",
    "ax.axvline(0,ls=\"--\",c=\"red\")\n",
    "ax.set_xlim(-5,5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3b9754",
   "metadata": {},
   "source": [
    "We want to create a table showing MMP transforms, and the associated distributions of activity differences (Deltas).  To create this table we need to have routines that will generate images and save them as base64 encoded strings that can be displayed in an HTML table. A lot of tricks here were borrowed from an \"Is Life Worth Living\" blog [post](https://iwatobipen.wordpress.com/2020/01/17/draw-rdkit-mol-reaction-object-on-html-without-static-png-image-rdkit-memo/).  Thanks Pen!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c5dd778",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rxn_to_base64_image(rxn):\n",
    "    \"\"\"\n",
    "    Convert an RDKit reaction to an image\n",
    "    \"\"\"\n",
    "    drawer = rdMolDraw2D.MolDraw2DCairo(300, 150)\n",
    "    drawer.DrawReaction(rxn)\n",
    "    drawer.FinishDrawing()\n",
    "    io.BytesIO()\n",
    "    text = drawer.GetDrawingText()\n",
    "    im_text64 = base64.b64encode(text).decode('utf8')\n",
    "    img_str = f\"<img src='data:image/png;base64, {im_text64}'/>\"\n",
    "    return img_str\n",
    "\n",
    "def strippplot_base64_image(dist):\n",
    "    \"\"\"\n",
    "    Plot a distribution as a seaborn stripplot and save the resulting image as a base64 image. \n",
    "    \"\"\"\n",
    "    sns.set(rc={'figure.figsize': (3, 1)})\n",
    "    sns.set_style('whitegrid')\n",
    "    ax = sns.stripplot(x=dist)\n",
    "    ax.axvline(0,ls=\"--\",c=\"red\")\n",
    "    ax.set_xlim(-5,5)\n",
    "    s = io.BytesIO()\n",
    "    plt.savefig(s, format='png', bbox_inches=\"tight\")\n",
    "    plt.close()\n",
    "    s = base64.b64encode(s.getvalue()).decode(\"utf-8\").replace(\"\\n\", \"\")\n",
    "    return '<img align=\"left\" src=\"data:image/png;base64,%s\">' % s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a77855",
   "metadata": {},
   "source": [
    "Generate images to go into the HTML table and add them to **mmp_df**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "093a247d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98e84faed70a48ca8da42c55200217de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/103 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a01449f06cc04b71ad25327a0b3444e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/103 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mmp_df['MMP Transform'] = mmp_df.rxn_mol.progress_apply(rxn_to_base64_image)\n",
    "mmp_df['Delta Distribution'] = mmp_df.Deltas.progress_apply(strippplot_base64_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20b1eb9",
   "metadata": {},
   "source": [
    "Show a table of MMPs with associated activity distributions. Change **rows_to_show** below to change the number of rows show in the table.  The variable **ascending** in the table below controls the sort order for the table.  If **ascending** is set to **True**, MMPs that reduce the hERG IC50 are shown at the top of the table. If we set **ascending** to **False**, MMPs that increase hERG activity will be shown at the top. The table shows \n",
    "- the MMP\n",
    "- the number of times the MMP occurs in the dataset\n",
    "- the mean of the difference in pIC50 between molecules containing that MMP\n",
    "- a stripplot showing the differences\n",
    "\n",
    "If we look at the first row in the table (index 41 in the far left column), we see that when chlorobenzyl is replaced by benzyl the hERG IC50 is consistently reduced.  If we havea molecule with a chlorobenzyl group that has hERG activity, we might think aobut replacing the chlorobenzyl with benzyl. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a731e4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMP Transform</th>\n",
       "      <th>Count</th>\n",
       "      <th>mean_delta</th>\n",
       "      <th>Delta Distribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>8</td>\n",
       "      <td>-1.10</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>6</td>\n",
       "      <td>-0.91</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>8</td>\n",
       "      <td>-0.72</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>7</td>\n",
       "      <td>-0.62</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>6</td>\n",
       "      <td>-0.51</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>8</td>\n",
       "      <td>-0.51</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>10</td>\n",
       "      <td>-0.50</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td>9</td>\n",
       "      <td>-0.48</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td><img src='data:image/png;base64, iVBORw0KGgoAAAANSUhEUgAAASwAAACWCAIAAADrOSKFAAAABmJLR0QA/wD/AP+gvaeTAAAP3UlEQVR4nO3deVhUVR8H8N8giJqiaZoVCoqluWHSQqlUZLYRiYbrA5KFgCAhj8skDy7lAkUqovKoaLlgmqi4tEmbGkVaagUkCoplCSWikMg65/3jvN7mHUjRuff+5o3v5y+H7JwzMt97595z7u8YhBAEAHzsuAcA0NQhhADMEEIAZgghADOEEIAZQgjADCEEYIYQAjBDCAGYIYQAzBBCAGYIIQAzhBCAGUIIwAwhBGCGEAIwQwgBmCGEAMwQQgBmCCEAM4QQgBlCCMAMIQRghhACMEMIAZghhADMEEIAZgghADOEEIAZQgjADCEEYIYQAjBDCAGYIYQAzBBCAGYIIQAzhBCAGUIIwAwhBGCGEAIwQwgBmCGEAMwQQgBmCCEAM4QQgBlCCMAMIQRghhACMLPnHkADUlNTY2JiXFxcnJ2d1WqzqKgoPz/faDSGhYWp1SZopLq6es+ePTt27FC9ZV9fXz8/v+bNm6veslWEjVm5cqWm7/f111/nfotwHd7e3v369dPit9+vXz8vLy+TycT9Fv+HQQihxbu9OSaTyd3dPTs729XVNTw8/I477lCr5eLi4tWrV+fl5XXp0iU/P9/mjoVwVVpamr+/f9u2bePj41u3bq1iy5WVlUaj8fz58++9996YMWNUbNla3EeB/7FmzRoicnZ2vnjxouqNX7lypWfPnkS0ePFi1RsHVVy5cqVbt25ElJycrEX7ygfsr7/+0qL9m2NDZ8Ly8vKePXueO3dOOVAVFBTk5eVZ37Kbm5uM3wcffODj4+Pk5JSXl9e5c2frWwZ1zZ8/PzY2tk+fPseOHbO3tyeijz76SJWP6LPPPktEJpPJ09Pz8OHDc+bMmTt3rvXNqoP7KPC3adOmEdEjjzyifGVfsGCBKu9x5syZSi/PPPMMEYWEhDC9S/hHZ8+eld8/9+3bp/zQYDCo8hmora2VDWZmZhoMhpYtWxYWFjK9UUu2ciYsKCjo06dPTU1NVlbWAw88IH/4/vvvv/vuu9Y3PmrUqKCgIPnn48eP9+/fv66u7tChQx4eHtY3DmoJDAzcuHHjyJEj09LSlB/6+PiYTCbrG9+7d6+d3X8n5MaMGbN169axY8du3rzZ+pZVwH0U+K/nn3+eiF5++WUd+oqKiiKiwYMH29pdsqbsm2++MRgMjo6OJ0+e1LqvX3/99ZZbbiGi/fv3a91XY9hECD/99FMiatOmze+//65Dd6WlpR07diSibdu26dAdXJfJZHrooYeIKCYmRp8e58yZQ0T33XdfXV2dPj1eA38Ia2pq+vbtS0Rvvvmmbp0mJycTUZcuXS5fvqxbp/BP5EVH586dL126pE+PFRUVLi4uRJSSkqJPj9fAH8LExEQicnNzq6ys1K3T2tpad3d3Ipo/f75unUKDysvL77zzTiLasGGDnv3KC8JOnTppMR92Q5hDWFJS0qFDByLatWuXzl1//vnnRNSqVaszZ87o3DWYe+2114jI09NT/0t0Ly8vIpo+fbrO/VpgDuHkyZOJ6IknnmDpfeTIkUQUEBDA0jsIIU6dOtWiRQuDwZCVlaV/70eOHLGzs2vevHleXp7+vSs4Q5iTk2Nvb29vb//jjz+yDODMmTOtWrUyGAwHDx5kGQCMGDGCiAIDA7kGMHHiRCLy9fXlGoDgDeGwYcOIKDIyknEMMTExROTh4WELd8maGnlF0Lp1699++41rDMXFxW3btqWrS3NYsIVQPqhy6623nj9/nmsMQojLly936dKFiNavX884jCaotra2f//+RLRgwQLekcTHxxPRvffeW11dzTIAnhBWVVXdfffdRLR8+XKWAZhbv349Ed1+++263R8HIcSKFSuIqFu3bleuXOEdSVVV1T333ENEiYmJLAPgCeHChQuJqHfv3jU1NSwDMGcymQYNGkREs2bN4h5LU3HhwoXbbruNiLZv3849FiGE2LVrl/xe9ueff+rfO0MIi4qKnJyciOiTTz7Rv/cGfffdd/Iu2YkTJ7jH0iS8+uqrRPT4449zD+RvTz31FBGFh4fr3zVDCCdMmEBEfn5++nd9DYGBgUQ0YsQI7oH8++Xm5jo4ODRr1uzYsWPcY/mbMqoffvhB5671DuH3339vm+cc5fxs/hwNaOHpp58morCwMO6BWJoyZQoReXt769yvriE0mUyDBw8mIqPRqGe/jTR//nwikk9UcY/lX2v37t2MV1/Xplyp7tixQ89+dQ3hxo0bbfk+ZGVlZY8ePYho5cqVGnURFRX1/zshGRcXd/ToUWtaUO5DLl26VK1RqWv58uVE1L17d43u2WZmZm7dutXih/qFsKKiomvXrkT0zjvv6NbpjZKPk7Zv31712cvMzMyYmJiQkJDExMTNmzer27gOvv76a4PBYGdnFxAQUFRUdHONvPXWW7wzctelzF4uWrRI9cajo6MTEhJiY2MnTZqkPOkv9AxhbGwsEQ0cONDGTwVPPvkkEUVFRaneclZWlre3d1pamo3/CzSotLR05syZjo6Oco3LnDlzbvSpF1tYm9IYn332mUbreC5fvhwZGRkUFGTxTbCB8hYZGRmZmZk385T+PysrK0tOTq6qqtq/f/+QIUPUbVxdOTk5AwYMEEJMmjSpU6dO1/7LHTp0qK6uvm6bbm5uw4cPDw0NnTt37owZM9atWyerGN0QtaqtqCUjI2Po0KGN//vBwcEpKSk+Pj579uzRblSq8PPzS09PHzhwoCz4cG3t27evqam57l+bMGHC6dOn9+/f36FDh06dOj333HN//7f6eZUFl1TXvXv3Xr16yS4qKiri4uKCgoLUPdLctODg4MjISOX45O7uLteyXZeXl9cdjSAXKMtHdW76gR0tfinWyMjIuKHxr1mzxsnJ6eOPP5YvDx8+7OXlpf98QINyc3M9PT2VdfxffPFFmzZtGlmctm/fvo35DOTm5gohTCZT/Q9AA8fjYcOGyQocKlLOhAcPHhwyZMilS5cWLlxYVlY2btw4+fWP0ZEjR9auXWtvbx8REeHk5JSTk5OTkyOEmDx5sqyCcQ2NPxPS1VPZTZ/QBGsOL168GBcXt3Tp0qqqqnbt2hmNxhv9RnPo0KGysrKkpCQ5Lb5hw4YDBw5MnTpVfv3jFR0dnZWVtXnzZnn3funSpeXl5R4eHj4+Ptf9fxt5JpSfpYZ/+7odbGbPnk1mVT1sZD6g/qzJY489RkRTp05lHJUNsv7GTHFxcbt27Yjoww8/FGbzATt37lR7sDdGfj1WZk3kQUG3ikeC5e7ounXrhNl8gEa1lhtp06ZNZDZrsm3bNnlsKykpYRyVbbJ+iiIhIYGIevXqJe+Oaj0f0BhVVVWyMLScNamtrZXbYMTFxek2Bl3nCZVPvKzqoXziuZ5msjguaF2DHZRP/JIlS4TZJ16L+YBGkrMmynEhKSlJ/+OC3itm5IWEUhJbu/mAxrCYNXnjjTds4Rvyv5v87teuXbs//vhDaDkf0BjKrAnvN2TmtaPZ2dmywsVPP/2k80h++eUXWdviwIED4h9qsIMW5E4EoaGh8uXw4cOJiOVW+SuvvEJEPj4+8mVERAT969eOSrIi/fDhw+XL0NBQIho6dKjOw/D39yei8ePHy5cBAQFENHLkSJ2H0QT9/PPP5k9RFBQUyFpP3377rZ7DOHLkSLNmzZo3b378+HEhRE5OjhyV/hWPOJ8nlFNGStXDvXv36jaGr776Su4KIusd6lmDHUS9nQiMRiPpXvVQ1jucNm2afCknTiIiInQbgILnyfpFixYRUe/eveXV8JIlS4ioR48e+tT/rauru//+++nqrr3612CH0tJSefWVlpYmhCgvL5cbwm7atEmfAWzZsoXMKv+mp6dTk3qyXpjVmElKShJC1NTU9OnTh4gSEhJ06H3VqlVkVgNf/xrsIK7ui961a1f5W1i3bh0R3XXXXTps31lRUeHq6kpEq1evFmbPdixbtkzrrhvEVm1t586dZFZtLSMjQ86Qnjt3TtN+L126JLcHlU+UcNVgB4udCOrq6h588EEiio2N1bpruT3ogAED5KMMcXFx1ASrrUmy7uiUKVPkS7mkNTg4WNNOo6OjiWjQoEHy8mPWrFmEuqNMLHYikItyWrRocfr0ae06VfZF+/LLL4UQRUVFcpZCWdSqP/4K3Mr9qJMnTzo6OtrZ2R0+fFijHi264K3BDqLeTgTjx48nIn9/f+16HDt2LBGNHj1avnzppZeI6IUXXtCux+ti3osiPDzcfGbG4jSlOouTLXsNdlCOg/IJhrNnz5qfplQnT7YtW7aUJ1vsRSGEEBcuXJDzE+np6aLeBZu6LC47baEGO4h6VwTz5s0zv2BTkXLZOXv2bGG2fmvGjBnqdnSj+PcnXLZsmVytJ+cnLG5dqsXiBqzt1GAHi50IlFuXq1atUrejtWvXmt+ATU1NJexPKCmreOPj44XZJN68efNU7GXx4sXmU5Hy/rgt1GAHUW8ngq1btxJRx44dS0tL1eqirKxMTkWmpqYKs516165dq1YXN40/hKLenvXKcpbCwkJV2i8pKWnfvr2yKEdZpytnioFd/Z0IHn30USKKjo5Wq4sZM2YQ0cMPPyxvN1g83crLJkIohPD19SWiiRMnypejRo0ionHjxqnSeEhIiPnyVBuswQ4WOxEcPXq0WbNmDg4OcmGnlQoKCuRdcbk81WLtPjtbCWF+fr78Zzp06JAQorCwsGXLlgaDwfrVtKdOnZK/Tlnkw2L1MNgOi50IgoODiWjMmDHWtyxX5ysPaqh7iLeerYRQCDF9+nTzLwwrVqzYvXu3Ki3v27fv7bffln+2eI4GbIfFTgTFxcVRUVGqPPBdUlISHR0tL3YyMzPVvdixng2FULl01q42rsUTpWBrtK48pNz2mzt3rhbt3xwbCqEQIiUlhYicnZ21uGtcUVFhXlsBbJDWOxGsXr1afsB0WCbeeA0U/2VkMpnc3d2zs7NdXV3Dw8PliVEVxcXFq1atOnHiRLdu3fLy8hwcHNRqGdS1ffv2F198sW3btvHx8bLWgVoqKyuNRuP58+e3bNkyevRoFVu2FvdRwJKcwdOOfIYQbJm3t7ecOlZdv379vLy89Hx0uDFs60wopaamxsTEuLi4ODs7q9VmUVFRfn6+0WgMCwtTq03QSHV19Z49e3bs2KF6y76+vn5+fo0sra0bWwwhQJNixz0AgKYOIQRghhACMEMIAZghhADMEEIAZgghADOEEIAZQgjADCEEYIYQAjBDCAGYIYQAzBBCAGYIIQAzhBCAGUIIwAwhBGCGEAIwQwgBmCGEAMwQQgBmCCEAM4QQgBlCCMAMIQRghhACMEMIAZghhADMEEIAZgghADOEEIAZQgjADCEEYIYQAjBDCAGYIYQAzBBCAGYIIQAzhBCAGUIIwAwhBGCGEAIwQwgBmCGEAMwQQgBmCCEAs/8AnsqMqwzi+c0AAADOelRYdHJka2l0UmVhY3Rpb25QS0wgcmRraXQgMjAyMi4wOS41AAB4nHu/b+09AXUGBiYGBGCEYhBgBmJBoPx7oDoQnxcqDlLPAcUNjAwsIJpBB6RLi80BzMFOMWAR5AbaxsjkwcPE7MHDzOLBw8LKwMLmwcPG7sHDzujBI84CtVNMCOgQYRyOYYdiUhxDwBUePKwgZyA7weo+1DouIE4pzc2t9ElMSs0BB5eWAJDIzc/xzCtLLSrOzM9zy0lMZ2SBBubQkRQWEwGGsTgQAwBI/kkRjf4xyQAAAER6VFh0UmVhY3Rpb25TbWFydHMgcmRraXQgMjAyMi4wOS41AAB4nItWNojVjVY2izW0ApEIQgMsqokkamhnF41LNbIyAGVUFMReOsvlAAAAAElFTkSuQmCC'/></td>\n",
       "      <td>7</td>\n",
       "      <td>-0.48</td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_to_show = 10\n",
    "ascending = True\n",
    "mmp_df.sort_values(\"mean_delta\",inplace=True,ascending=ascending)\n",
    "HTML(mmp_df[['MMP Transform','Count',\"mean_delta\",\"Delta Distribution\"]].round(2).head(rows_to_show).to_html(escape=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ce8a2a",
   "metadata": {},
   "source": [
    "Once we scan the table above and identify an interesting MMP, we may want to look at all the molecules containing that MMP.  To do that, we need need to go into **delta_df** and find the corresponding rows.  To make it easy to view the examples with mols2grid, we'll put the MMPs in alternating rows in a new talble.  The function below creates a new table with all the rows in **delta_df** having the the value **query_idx** in the **idx** column. The value we use for **query_idx** is the number in the far left column in the table above.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5abe3434",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_examples(delta_df, query_idx):\n",
    "    example_list = []\n",
    "    for idx,row in delta_df.query(\"idx == @query_idx\").sort_values(\"Delta\",ascending=False).iterrows():\n",
    "        smi_1, name_1, pIC50_1 = row.SMILES_1, row.Name_1, row.pIC50_1\n",
    "        smi_2, name_2, pIC50_2 = row.SMILES_2, row.Name_2, row.pIC50_2\n",
    "        tmp_list = [(smi_1, name_1, pIC50_1),(smi_2, name_2, pIC50_2)]\n",
    "        tmp_list.sort(key=itemgetter(0))\n",
    "        example_list.append(tmp_list[0])\n",
    "        example_list.append(tmp_list[1])\n",
    "    example_df = pd.DataFrame(example_list,columns=[\"SMILES\",\"Name\",\"pIC50\"])\n",
    "    return example_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babcdef3",
   "metadata": {},
   "source": [
    "Show molecules that include a particular MMP transform. Change the value of **query_idx** to view molecules containing a particular MMP. Recall that **query_idx** is the far left column in the fancy table above showing the MMPs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2c586b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf1fb6a1195f4c989a306edae7ecb7f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MolGridWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    /* Some CSS to integrate with Jupyter more cleanly */\n",
       "    div.output_subarea {\n",
       "        /* Undo an unfortunate max-width parameter\n",
       "        that causes the output area to be too narrow\n",
       "        on smaller screens. */\n",
       "        max-width: none;\n",
       "\n",
       "        /* Align the table with the content */\n",
       "        padding: 0;\n",
       "\n",
       "        /* Let it breathe */\n",
       "        margin-top: 20px;\n",
       "    }\n",
       "</style>\n",
       "\n",
       "<iframe class=\"mols2grid-iframe\" frameborder=\"0\" width=\"100%\"\n",
       "    \n",
       "    \n",
       "    allow=\"clipboard-write\"\n",
       "    \n",
       "    \n",
       "    sandbox=\"allow-scripts allow-same-origin allow-downloads allow-popups allow-modals\"\n",
       "    \n",
       "    srcdoc=\"\n",
       "\n",
       "&lt;html lang=&quot;en&quot;&gt;\n",
       "    &lt;head&gt;\n",
       "        &lt;meta charset=&quot;UTF-8&quot; /&gt;\n",
       "        &lt;meta http-equiv=&quot;X-UA-Compatible&quot; content=&quot;IE=edge&quot; /&gt;\n",
       "        &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width, initial-scale=1.0&quot; /&gt;\n",
       "        &lt;title&gt;Document&lt;/title&gt;\n",
       "\n",
       "\n",
       "\n",
       "        &lt;style&gt;\n",
       "            /**\n",
       " * General styling\n",
       " */\n",
       "body {\n",
       "    font-family: &#x27;DejaVu&#x27;, sans-serif;\n",
       "}\n",
       "h1,h2,h3,h4 {\n",
       "    margin: 0 0 10px 0;\n",
       "}\n",
       "h1 {\n",
       "    font-size: 26px;\n",
       "}\n",
       "h2 {\n",
       "    font-size: 20px;\n",
       "    font-weight: 400;\n",
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       "h3 {\n",
       "\tfont-size: 16px;\n",
       "}\n",
       "p {\n",
       "    margin: 0 0 10px 0;\n",
       "}\n",
       "\n",
       "\n",
       "/* Remove body margin inside iframe */\n",
       "body.m2g-inside-iframe {\n",
       "    margin: 0;\n",
       "}\n",
       "\n",
       "/* In-cell text */\n",
       "#mols2grid .data:not(.data-img) {\n",
       "    height: 16px;\n",
       "    line-height: 16px;\n",
       "}\n",
       "/* Text truncation */\n",
       "#mols2grid .data {\n",
       "    /* Break text into multiple lines (default for static)... */\n",
       "    word-wrap: break-word;\n",
       "\n",
       "    /* ...or truncate it (default for interactive). */\n",
       "    overflow: visible;\n",
       "    white-space: normal;\n",
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       "\n",
       "\n",
       "/**\n",
       " * Popover\n",
       " * - - -\n",
       " * Note: this is a bootstrap variable which is not namespaced.\n",
       " * To avoid any contamination, we only style it when the\n",
       " * x-placement parameter is set.\n",
       " */\n",
       ".popover[x-placement] {\n",
       "    font-family: &#x27;DejaVu&#x27;, sans-serif;\n",
       "    background: white;\n",
       "    border: solid 1px rgba(0,0,0,.2);\n",
       "    font-size: 12px;\n",
       "    padding: 10px;\n",
       "    border-radius: 5px;\n",
       "    box-shadow: 0 0 20px rgba(0,0,0,.15);\n",
       "    user-select: none;\n",
       "}\n",
       ".popover[x-placement] h3 {\n",
       "    margin: 0;\n",
       "}\n",
       ".popover[x-placement] .arrow {\n",
       "    width: 10px;\n",
       "    height: 10px;\n",
       "    background: #fff;\n",
       "    border: solid 1px rgba(0,0,0,.2);\n",
       "    box-sizing: border-box;\n",
       "    position: absolute;\n",
       "    transform-origin: 5px 5px;\n",
       "    clip-path: polygon(0 0, 100% 0, 100% 100%);\n",
       "}\n",
       ".popover[x-placement=&#x27;left&#x27;] .arrow {\n",
       "    transform: rotate(45deg);\n",
       "    top: 50%;\n",
       "    right: -5px;\n",
       "}\n",
       ".popover[x-placement=&#x27;right&#x27;] .arrow {\n",
       "    transform: rotate(-135deg);\n",
       "    top: 50%;\n",
       "    left: -5px;\n",
       "}\n",
       ".popover[x-placement=&#x27;top&#x27;] .arrow {\n",
       "    transform: rotate(135deg);\n",
       "    left: 50%;\n",
       "    bottom: -5px;\n",
       "}\n",
       ".popover[x-placement=&#x27;bottom&#x27;] .arrow {\n",
       "    transform: rotate(-45deg);\n",
       "    left: 50%;\n",
       "    top: -5px;\n",
       "}\n",
       "            /* Note: #mols2grid is the table element */\n",
       "#mols2grid {\n",
       "    border-collapse: collapse;\n",
       "    border-spacing: 0px;\n",
       "    /* Compensate for gap so table stays aligned with content above */\n",
       "    margin: 0px;\n",
       "}\n",
       "#mols2grid, tr, td {\n",
       "    border: none;\n",
       "}\n",
       "#mols2grid tr {\n",
       "    page-break-inside: avoid !important;\n",
       "}\n",
       "\n",
       "/* Cell */\n",
       "#mols2grid td {\n",
       "    border: 1px solid #cccccc;\n",
       "    text-align: center;\n",
       "    vertical-align: top;\n",
       "    max-width: 200px;\n",
       "    width: 200px;\n",
       "    font-family: &#x27;DejaVu&#x27;, sans-serif;\n",
       "    font-size: 12px;\n",
       "    margin: 0;\n",
       "    padding: 10px;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       "/* Call focus state */\n",
       "#mols2grid td:focus-within,\n",
       "#mols2grid td:focus {\n",
       "    outline: solid 2px #555;\n",
       "    border-color: transparent;\n",
       "}\n",
       "\n",
       "/* ID */\n",
       "#mols2grid .data-mols2grid-id {\n",
       "    position: absolute;\n",
       "    top: 0;\n",
       "    left: 0;\n",
       "    padding: 5px;\n",
       "}\n",
       "\n",
       "/* Tooltip margin */\n",
       "/* Adjusted so it plays well with the extruded outline */\n",
       ".popover[x-placement] {\n",
       "    margin: 1px 2px 2px 1px;\n",
       "}\n",
       "\n",
       "\n",
       "#mols2grid td div img {\n",
       "    max-width: px;\n",
       "    width: px;\n",
       "    height: auto;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       "            /* Custom CSS */\n",
       "            \n",
       "        &lt;/style&gt;\n",
       "        &lt;script src=&quot;https://code.jquery.com/jquery-3.5.1.slim.min.js&quot; integrity=&quot;sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj&quot; crossorigin=&quot;anonymous&quot;&gt;&lt;/script&gt;\n",
       "        &lt;script&gt;\n",
       "    // Set iframe height to fit content.\n",
       "    function fitIframe(iframe) {\n",
       "        // Ignore when there&#x27;s no iframe\n",
       "        if (!iframe) return\n",
       "\n",
       "        // Only fit height when no specific height was given.\n",
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       "</iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_idx = 84\n",
    "example_df = find_examples(delta_df, query_idx)\n",
    "mols2grid.display(example_df,n_cols=4,template='static',prerender=True,size=(200,200),\n",
    "                 subset=[\"img\",\"pIC50\",\"Name\"],\n",
    "                 transform={\"pIC50\": lambda x: f\"{x:.2f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77193c55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a15d497c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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